Application of Machine Learning Techniques to Improve Multi-Radar Multi-Sensor (MRMS) Precipitation Estimates in the Western United StatesSource: Artificial Intelligence for the Earth Systems:;2023:;volume( 002 ):;issue: 002Author:Osborne, Andrew P.
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Zhang, Jian
,
Simpson, Micheal J.
,
Howard, Kenneth W.
,
Cocks, Stephen B.
DOI: 10.1175/AIES-D-22-0053.1Publisher: American Meteorological Society
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| contributor author | Osborne, Andrew P. | |
| contributor author | Zhang, Jian | |
| contributor author | Simpson, Micheal J. | |
| contributor author | Howard, Kenneth W. | |
| contributor author | Cocks, Stephen B. | |
| date accessioned | 2024-12-24T14:23:47Z | |
| date available | 2024-12-24T14:23:47Z | |
| date copyright | 01 Apr. 2023 | |
| date issued | 2023 | |
| identifier other | aies-AIES-D-22-0053.1.pdf | |
| identifier uri | http://yetl.yabesh.ir/yetl1/handle/yetl/4300667 | |
| language | English | |
| publisher | American Meteorological Society | |
| title | Application of Machine Learning Techniques to Improve Multi-Radar Multi-Sensor (MRMS) Precipitation Estimates in the Western United States | |
| type | Journal Paper | |
| journal volume | 2 | |
| journal issue | 2 | |
| journal title | Artificial Intelligence for the Earth Systems | |
| identifier doi | 10.1175/AIES-D-22-0053.1 | |
| journal lastpage | 220053 | |
| tree | Artificial Intelligence for the Earth Systems:;2023:;volume( 002 ):;issue: 002 | |
| contenttype | Fulltext |